Apriori
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Python Implementation of Apriori Algorithm for finding Frequent sets and Association Rules
Python Implementation of Apriori Algorithm
Set up
Edit without local environment setup
Acknowledgements
The code attempts to implement the following paper:
Agrawal, Rakesh, and Ramakrishnan Srikant. "Fast algorithms for mining association rules." Proc. 20th int. conf. very large data bases, VLDB. Vol. 1215. 1994.
Interactive Streamlit App
To view a live interactive app, and play with the input values, please click here. This app was built using Streamlit 😎, the source code for the app can be found here
CLI Usage
To run the program with dataset provided and default values for minSupport = 0.15 and minConfidence = 0.6
python apriori.py -f INTEGRATED-DATASET.csv
To run program with dataset
python apriori.py -f INTEGRATED-DATASET.csv -s 0.17 -c 0.68
Best results are obtained for the following values of support and confidence:
Support : Between 0.1 and 0.2
Confidence : Between 0.5 and 0.7
List of files
- apriori.py
- INTEGRATED-DATASET.csv
- README(this file)
The dataset is a copy of the “Online directory of certified businesses with a detailed profile” file from the Small Business Services (SBS)
dataset in the NYC Open Data Sets <http://nycopendata.socrata.com/>_
License
MIT-License
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